- Home
- People
- Associate Investigators
- Gael Martin
Professor Gael Martin
Monash University
Gael Martin is a Professor of Econometrics in the Department of Econometrics and Business Statistics and PhD Director for the Department.
An up-to-date list of publications, work in progress, ARC grant projects, and editorial, supervision and teaching responsibilities can be found at the following personal home page web address:
http://users.monash.edu.au/~gmartin/
This personal home page is regularly maintained.
(The Monash University Faculty profile page is often out-of-date.)
Research Interests:
Bayesian Econometrics
Count Time Series
Financial Econometrics
Forecasting
Simulation Methods
Qualifications:
BA (University of Melbourne)
ASSOCIATE IN MUSIC AUSTRALIA (A.MUS.A)
BEc (Hons 1), Monash University
MEc (Monash University)
PhD (Monash University)
Publications
Invited talks, refereed proceedings and other conference outputs
Martin, G. M.
(2021). Loss-Based Variational Bayes Prediction.
41st International Symposium on Forecasting.
Martin, G. M.
(2021). Loss-Based Variational Bayes Prediction.
European Seminar on Bayesian Econometrics.
Martin, G. M.
(2021). New Approaches to Probabilistic Prediction.
Shaping the Future of Data Science: Research Spotlight Series.
Martin, G. M.
(2019). Focused Bayesian Prediction.
AutoStat Research Week: Frontiers in Research & Practice in Statistics.
Martin, G. M.
(2019). Focused Bayesian Prediction.
13th RCEA Bayesian Econometrics Workshop.
Martin, G. M.
(2019). Looking into the Future with the Reverend! The Future of Bayesian Forecasting.
The 39th International Symposium on Forecasting.
Nadarajah, K., Martin G. M., & Poskitt D. S.
(2019). Mean Correction in Mis-Specified Fractionally Integrated Models.
Economics, University of Manchester.
Frazier, D. T., Loaiza-Maya R., & Martin G. M.
(2019). Focused Bayesian Prediction.
The 12th International Conference on Monte Carlo Methods & Applications.
Fildes, R., Makridakis S., Martin G. M., Ruiz E., & Hyndman R. J.
(2019). Advancing forecasting research and practice.
39th International Symposium on Forecasting.
Journal Articles
Frazier, D. T., Loaiza-Maya R., & Martin G. M.
(2021). A Note on the Accuracy of Variational Bayes in State Space Models: Inference and Prediction.
arXiv. arXiv:2106.12262v2.
Loaiza-Maya, R., Martin G. M., & Frazier D. T.
(2021). Focused Bayesian prediction.
Journal of Applied Econometrics. 36(5), 517-543. doi: 10.1002/jae.2810
Martin, G. M., Loaiza-Maya R., Frazier D. T., Maneesoonthorn W., & Hassan A. Ramirez
(2020). Optimal probabilistic forecasts: When do they work?.
arXiv. arXiv:2009.09592v1,
Martin, G. M., Frazier D. T., & Robert C. P.
(2020). Computing Bayes: Bayesian Computation from 1763 to the 21st Century.
arXiv. arXiv:2004.06425v2,
Maneesoonthorn, W., Martin G. M., & Forbes C. S.
(2020). High-Frequency Jump Tests: Which Test Should We Use?.
arXiv. arXiv:1708.09520,
Harris, D., Martin G. M., Perera I., & Poskitt D.. S.
(2019). Construction and Visualization of Confidence Sets for Frequentist Distributional Forecasts.
Journal of Computational and Graphical Statistics. 28(1), 92-104. doi: 10.1080/10618600.2018.1476252
Loaiza-Maya, R., Martin G. M., & Frazier D. T.
(2019). Focused Bayesian Prediction.
arXiv. arXiv:1912.12571v1.
Martin, G. M., McCabe B. P. M., Frazier D. T., Maneesoonthorn W., & Robert C. P.
(2019). Auxiliary Likelihood-Based Approximate Bayesian Computation in State Space Models.
Journal of Computational and Graphical Statistics. 28(3), 508-522. doi: 10.1080/10618600.2018.1552154
Frazier, D. T., Martin G. M., Robert C. P., & Rousseau J.
(2018). Asymptotic properties of approximate Bayesian computation.
Biometrika. 105(3), 593 - 607. doi: 10.1093/biomet/asy027
Maneesoonthorn, W., Forbes C. S., & Martin G. M.
(2017). Inference on Self-Exciting Jumps in Prices and Volatility Using High-Frequency Measures.
Journal of Applied Econometrics. 32(3), 504 - 532. doi: 10.1002/jae.2547
Poskitt, D.. S., Martin G. M., & Grose S. D.
(2017). BIAS CORRECTION OF SEMIPARAMETRIC LONG MEMORY PARAMETER ESTIMATORS VIA THE PREFILTERED SIEVE BOOTSTRAP.
Econometric Theory. 33(03), 578 - 609. doi: 10.1017/S0266466616000050
Grose, S. D., Martin G. M., & Poskitt D. S.
(2015). Bias Correction of Persistence Measures in Fractionally Integrated Models.
Journal of Time Series Analysis. 36(5), 721-740. doi: 10.1111/jtsa.12116
Poskitt, D.S.., Grose S. D., & Martin G. M.
(2015). Higher-order improvements of the sieve bootstrap for fractionally integrated processes.
Journal of Econometrics. 188(1), 94 - 110. doi: 10.1016/j.jeconom.2015.03.045
Technical reports and unrefereed outputs
Nadarajah, K., Martin G. M., & Poskitt D. S.
(Submitted). Optimal Bias Correction of the Log-periodogram Estimator of the Fractional Parameter: A Jackknife Approach.
Journal of Statistic Inference and Planning.
Leung, P., Forbes C. S., Martin G. M., & McCabe B.
(2020). Forecasting observables in state space models: does the choice of filter matter?.
